Ecosystems are in danger due to human-caused air, water, and soil pollution, so it is important to find the underlying causes of this issue and develop practical solutions. This study adds to environmental research gap by suggesting the load capability factor (LCF) and using it to look at the factors affectting environmental health. The load capacity factor simplifies monitoring environmental health by illustrating the distinction between ecological footprint and biocapacity. We examine the interplay between mobile phone users (Digitalization DIG), technological advancements (TEC), renewable energy use, economic growth, and financial development. This study assesses G8 economies’ data from 1990 to 2018, using a Cross-Section Improved Autoregressive Distributed Lag CS-ARDL estimator and a cointegration test. The data shows that green energy, TEC innovation, and DIG are all beneficial for natural health. Based on the results of this study, the G8 governments should focus on environmental policies that promote economic growth, increase the use of renewable energy sources, guide technological progress in key areas, and encourage the development of digital information and communications technologies that are better for the environment.

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Diseases of the Gastrointestinal (GI) tract significantly affect the quality of human life and have a high fatality rate. Accurate diagnosis of GI diseases plays a pivotal role in healthcare systems. However, processing large amounts of medical image data can be challenging for radiologists and other medical professionals, increasing the risk of inaccurate medical assessments. Computer‐aided Diagnosis systems provide help to doctors for rapid and accurate diagnosis, thus resulting in saving lives. Recently, many techniques are found in the literature that uses deep Convolutional Neural Network (CNN) models for accurate disease classification. However, they have limitations in their ability to detect deformation‐invariant features and lack robustness. The diseased region is highlighted, using attention‐based image generation and superimposition with original images. A lightweight deep CNN model is employed to get significant features. These features are further reduced using a Cosine similarity‐based technique. The proposed framework is assessed using the Kvasir dataset. To verify the effectiveness of the proposed framework, vast experiments are conducted. The overall accuracy of 97.68%, 99.02% precision, 96.37% recall, and an F‐measure of 97.68% are achieved using the 810 significant features. This reduction in features resulted in a significant reduction in classification time. The robustness of the framework can be observed not only in terms of considerable improvement in accuracy, but also in terms of precision as well as recall, and F‐measure.

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Many nations made pledges at the Paris climate conference to eventually become carbon neutral. As a result, the effects of eco-innovations (ECO), globalization (GLO), and economic growth (GDP) on CO2 emissions in a panel comprising India, Pakistan, Bangladesh, Nepal, Sri Lanka, and Bhutan are assessed in this work. This study employs a unique panel (QARDL) methodology to data from 1980Q1 to 2018Q4 for analysis. The purpose of this study is to find the relation between GDP, GLO, ECO and CO2. The results show that environmental quality is being harmed because of GLO and GDP. Climate-change-causing CO2 emissions are decreasing globally thanks to ECO. Furthermore, the Environmental Kuznets Curve (EKC) theory in developing nations has been confirmed by this work. This study implies that the selected South Asian countries should switch to renewable energy sources to improve environmental quality. In addition, governments will need to rethink their approach to global trade. Importing effective technologies for producing renewable energy should be a priority. The future looks bright for these nations, as rising environmental consciousness will likely lead to the adoption of stringent environmental rules.